AI-Based Predictive Maintenance Models in Smart Manufacturing Environments
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V6I4P124Keywords:
Predictive Maintenance, Industrial IoT (IIoT), Equipment Failure Prediction, Rare Event Modeling, Machine Learning For Maintenance, Condition Monitoring Data, Anomaly Detection Techniques, Supervised Learning Models, Unsupervised Clustering Methods, Self-Supervised Learning Approaches, Data Quality In IoT Systems, Failure Risk Assessment, Context-Aware Model Validation, Maintenance Lifecycle Analytics, Intelligent Maintenance Management SystemsAbstract
Equipment downtime incurs direct financial losses, reduced end-customer service levels, and negative environmental impact. Downtimes often result from equipment failures that could be prevented through proper maintenance planning. Predictive maintenance promises to minimize unplanned downtimes by exploiting the data generated by Industrial IoT devices. While these types of data are usually plentiful, their quality is sometimes unsatisfactory, making machine learning models difficult to develop. Available datasets for predictive maintenance build upon condition monitoring, and usually cover conditions that are rarely met in real workloads. Therefore, finding approaches for handling rare events is essential for training reliable failure prediction models. Observing the factors that impact these paradigms, several predictive-maintenance models can be proposed. Supervised learning methods can be applied for predicting future failures based on labelled data, while anomaly-detection models can be explored to identify abnormal use conditions. Unsupervised clustering techniques can help identify outliers in usage patterns, and self-supervised setups exploit the rich information available in sensors, but do not require predictions of rare failures. The proposed methods cover the full predictive-maintenance lifecycle, from identifying, collecting, and preprocessing data, to context-based training-validation-testing pipelines, to interpretation and hints on integration with maintenance management systems.
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